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Comprehensive Data-Driven Framework for Detecting and Classifying Non-Technical Distribution Losses
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DOI：10.1109/ACCESS.2024.3426940.png)
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摘要
En 中文
Non-technical losses (NTL) pose multiple challenges across distribution grids. This paper introduces a comprehensive framework combining data-driven methods to detect and categorize fraud due to meter tampering or direct connections while identifying potential culprits. The hybrid methodology utilizes grid and consumer-related data to obtain NTL curves through an energy balance approach, yielding indicators such as magnitude, duration, and other features. A Random Forest classifier trained with real historical cases of NTL achieves a weighted F1 score of 0.859, effectively labelling fraud types. Additionally, an unsupervised detection model, integrating clustering and correlation methods, enables accurate identification of tampered meters. The paper introduces two adjustable parameters enabling utilities to fine-tune meter tampering detection strategies based on economic considerations. The results demonstrate that true positives can be increased at the expense of increasing false positives. Accurate fraud identification is achieved using the Fuzzy C-Means algorithm, with an F1 score of 0.9. The algorithm is tested on grids with distributed generation, with a decrease of 10% on the predictive performance when half of the users are prosumers, demonstrating the methodology's promising performance in real-world scenarios.
Keyword:
Fraud
Classification algorithms
Meters
Voltage control
Support vector machines
Hardware
Clustering algorithms
Unsupervised learning
Classification
direct connection
false data injection (FDI)
fraud detection
hybrid algorithm
inspection strategies
meter tampering
non-technical losses (NTL)
unsupervised learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
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